count_corpus
时间: 2024-06-19 13:02:25 浏览: 7
count_corpus是一个统计语料库词频的工具。它可以读取一个文本文件并计算其中每个单词出现的次数,然后将结果输出为一个词频表。这个工具通常用于自然语言处理和文本挖掘领域中,可以帮助研究者快速了解一个语料库中单词的分布情况,以及对文本进行预处理和特征提取。如果你有需要统计文本中单词出现频率的需求,可以考虑使用count_corpus工具。
相关问题
Traceback (most recent call last): File "C:\Users\Administrator\Desktop\python程序\gensim古诗生成.py", line 84, in <module> main() File "C:\Users\Administrator\Desktop\python程序\gensim古诗生成.py", line 68, in main m = Model.initialize(config) File "C:\Users\Administrator\Desktop\python程序\gensim古诗生成.py", line 35, in initialize model = Word2Vec(ls_of_ls_of_c, config.size, File "C:\Users\Administrator\AppData\Roaming\Python\Python310\site-packages\gensim\models\word2vec.py", line 428, in __init__ self._check_corpus_sanity(corpus_iterable=corpus_iterable, corpus_file=corpus_file, passes=(epochs + 1)) File "C:\Users\Administrator\AppData\Roaming\Python\Python310\site-packages\gensim\models\word2vec.py", line 1499, in _check_corpus_sanity raise TypeError("Both corpus_file and corpus_iterable must not be provided at the same time") TypeError: Both corpus_file and corpus_iterable must not be provided at the same time出现这样问题如何解决
这个错误是由于在创建 Word2Vec 模型时同时提供了 `corpus_iterable` 和 `corpus_file` 参数,而这两个参数只能提供其中的一个。要解决这个问题,你需要检查你的代码,看看是否同时提供了这两个参数。
如果你想从文件中读取语料库,可以只提供 `corpus_file` 参数,如下所示:
```
model = Word2Vec(corpus_file='path/to/corpus.txt', size=100, window=5, min_count=5, workers=4)
```
如果你已经将语料库加载到内存中(即 `ls_of_ls_of_c` 变量),可以只提供 `corpus_iterable` 参数,如下所示:
```
model = Word2Vec(corpus_iterable=ls_of_ls_of_c, size=100, window=5, min_count=5, workers=4)
```
注意,这里的 `ls_of_ls_of_c` 是一个列表,其中每个元素都是一个句子(或文档)的词语列表,例如:
```
ls_of_ls_of_c = [['我', '爱', '中国'], ['中国', '是', '一个', '伟大', '的', '国家']]
```
在下面这段代码的基础上进行修改import math from collections import defaultdict corpus =["二价 二价 二价 四价 预约", "四价 四价 四价 九价 预约", "九价 九价 九价 九价 预约"] words = [] for sentence in corpus: words.append(sentence.strip().split()) # 进行词频统计 def Counter(words): word_count = [] for sentence in words: word_dict = defaultdict(int) for word in sentence: word_dict[word] += 1 word_count.append(word_dict) return word_count word_count = Counter(words) # 计算TF(word代表被计算的单词,word_dict是被计算单词所在句子分词统计词频后的字典) def tf(word, word_dict): return word_dict[word] / sum(word_dict.values()) # 统计含有该单词的句子数 def count_sentence(word, word_count): return sum([1 for i in word_count if i.get(word)]) # i[word] >= 1 # 计算IDF def idf(word, word_count): return math.log((len(word_count) / (count_sentence(word, word_count) + 1)),10) # 计算TF-IDF def tfidf(word, word_dict, word_count): return tf(word, word_dict) * idf(word, word_count) p = 1 for word_dict in word_count: print("part:{}".format(p)) p += 1 for word, cnt in word_dict.items(): print("word: {} ---- TF-IDF:{}".format(word, tfidf(word, word_dict, word_count))) print("word: {} ---- TF:{}".format(word, tf(word, word_dict))) print("word: {} ---- IDF:{}".format(word, idf(word, word_count))) print("word: {} ---- count_sentence:{}".format(word, count_sentence(word, word_count))),将IDF进行改进,其中自定义热度权重文件weight.txt中我想存入的是每一个文档的热度权重,改进的idf值就是总文档热度权重总和除以包含某词所在的文档的热度权重之和然后再取对数,请写出改进后的python代码
以下是改进后的代码:
```
import math
from collections import defaultdict
corpus =["二价 二价 二价 四价 预约", "四价 四价 四价 九价 预约", "九价 九价 九价 九价 预约"]
words = []
for sentence in corpus:
words.append(sentence.strip().split())
# 进行词频统计
def Counter(words):
word_count = []
for sentence in words:
word_dict = defaultdict(int)
for word in sentence:
word_dict[word] += 1
word_count.append(word_dict)
return word_count
word_count = Counter(words)
# 读取热度权重文件
with open('weight.txt', 'r') as f:
weight = [float(line.strip()) for line in f.readlines()]
# 计算IDF
def idf(word, word_count, weight):
count = count_sentence(word, word_count)
if count == 0:
return 0
else:
return math.log(sum(weight) / (weighted_count_sentence(word, word_count, weight) + 1), 10)
# 统计包含该单词的文档数
def count_sentence(word, word_count):
return sum([1 for i in word_count if i.get(word)])
# 统计包含该单词的文档的热度权重之和
def weighted_count_sentence(word, word_count, weight):
count = 0
for i in word_count:
if i.get(word):
count += weight[word_count.index(i)]
return count
# 计算TF-IDF
def tfidf(word, word_dict, word_count, weight):
return tf(word, word_dict) * idf(word, word_count, weight)
# 计算TF
def tf(word, word_dict):
return word_dict[word] / sum(word_dict.values())
# 输出结果
p = 1
for word_dict in word_count:
print("part:{}".format(p))
p += 1
for word, cnt in word_dict.items():
print("word: {} ---- TF-IDF:{}".format(word, tfidf(word, word_dict, word_count, weight)))
print("word: {} ---- TF:{}".format(word, tf(word, word_dict)))
print("word: {} ---- IDF:{}".format(word, idf(word, word_count, weight)))
print("word: {} ---- count_sentence:{}".format(word, count_sentence(word, word_count)))
```
相关推荐
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![zip](https://img-home.csdnimg.cn/images/20210720083736.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)